Literature DB >> 21221587

Phase resetting control based on direct phase response curve.

D V Efimov1.   

Abstract

The problem of controlled phase adjustment (resetting) for models of biological oscillators is considered. The proposed approach is based on oscillators excitation by a pulse, that results in the phase advancement or delay. Design procedure is presented for a series of pulses generation ensuring the required phase resetting. The solution is based on the direct phase response curve (PRC) approach. The notion of direct PRC is developed and non-local PRC model is proposed for oscillators. This model is more suitable for phase dynamics description under inputs excitation with sufficiently high amplitudes. The proposed model is used for controls design. Two control strategies are tested, the open-loop control (that generates a predefined table of instants of the pulses activation ensuring the resetting) and the feedback control (that utilizes information about the current phase value measured once per pulse application). The open-loop control is easier for implementation, the feedback control needs the estimation of the actual phase in the oscillating system. The algorithm of phase estimation is also presented. The conditions of the model and the controls validity and accuracy are determined. Performance of the obtained solution is demonstrated via computer simulation for two models of circadian oscillations and a model of heart muscle contraction. It is shown that in the absence of disturbances the open-loop and the feedback controls have similar performance. Additionally, the feedback control is insensitive to external disturbances influence. In these examples the presented scheme for phase values estimation demonstrates better accuracy than the conventional one.

Mesh:

Year:  2011        PMID: 21221587     DOI: 10.1007/s00285-010-0396-y

Source DB:  PubMed          Journal:  J Math Biol        ISSN: 0303-6812            Impact factor:   2.259


  12 in total

1.  Predicting the entrainment of reentrant cardiac waves using phase resetting curves.

Authors:  Leon Glass; Yoshihiko Nagai; Kevin Hall; Mario Talajic; Stanley Nattel
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2002-01-24

2.  Starting, stopping, and resetting biological oscillators: in search of optimum perturbations.

Authors:  Daniel B Forger; David Paydarfar
Journal:  J Theor Biol       Date:  2004-10-21       Impact factor: 2.691

3.  Some types of relaxation oscillations as models of all-or-none phenomena.

Authors:  G KARREMAN
Journal:  Bull Math Biophys       Date:  1949-12

4.  Synchrony in excitatory neural networks.

Authors:  D Hansel; G Mato; C Meunier
Journal:  Neural Comput       Date:  1995-03       Impact factor: 2.026

Review 5.  Limit cycle models for circadian rhythms based on transcriptional regulation in Drosophila and Neurospora.

Authors:  J C Leloup; D Gonze; A Goldbeter
Journal:  J Biol Rhythms       Date:  1999-12       Impact factor: 3.182

6.  A model for circadian rhythms in Drosophila incorporating the formation of a complex between the PER and TIM proteins.

Authors:  J C Leloup; A Goldbeter
Journal:  J Biol Rhythms       Date:  1998-02       Impact factor: 3.182

7.  Heart muscle contraction oscillation.

Authors:  G Karreman; C Prood
Journal:  Int J Biomed Comput       Date:  1995-01

8.  Phase locking, period-doubling bifurcations, and irregular dynamics in periodically stimulated cardiac cells.

Authors:  M R Guevara; L Glass; A Shrier
Journal:  Science       Date:  1981-12-18       Impact factor: 47.728

9.  Phase locking, period doubling bifurcations and chaos in a mathematical model of a periodically driven oscillator: a theory for the entrainment of biological oscillators and the generation of cardiac dysrhythmias.

Authors:  M R Guevara; L Glass
Journal:  J Math Biol       Date:  1982       Impact factor: 2.259

10.  Circadian phase resetting via single and multiple control targets.

Authors:  Neda Bagheri; Jörg Stelling; Francis J Doyle
Journal:  PLoS Comput Biol       Date:  2008-07-04       Impact factor: 4.475

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